**Name:**

stdp_synapse - Synapse type for spike-timing dependent

plasticity.

**Examples:**

multiplicative STDP [2] mu_plus = mu_minus = 1.0

additive STDP [3] mu_plus = mu_minus = 0.0

Guetig STDP [1] mu_plus = mu_minus = [0.0,1.0]

van Rossum STDP [4] mu_plus = 0.0 mu_minus = 1.0

**Description:**

stdp_synapse is a connector to create synapses with spike time

dependent plasticity (as defined in [1]). Here the weight dependence

exponent can be set separately for potentiation and depression.

**Parameters:**

tau_plus double - Time constant of STDP window, potentiation in ms

(tau_minus defined in post-synaptic neuron)

lambda double - Step size

alpha double - Asymmetry parameter (scales depressing increments as

alpha*lambda)

mu_plus double - Weight dependence exponent, potentiation

mu_minus double - Weight dependence exponent, depression

Wmax double - Maximum allowed weight

**Transmits:**

SpikeEvent

**References:**

[1] Guetig et al. (2003) Learning Input Correlations through Nonlinear

Temporally Asymmetric Hebbian Plasticity. Journal of Neuroscience

[2] Rubin, J., Lee, D. and Sompolinsky, H. (2001). Equilibrium

properties of temporally asymmetric Hebbian plasticity, PRL

86,364-367

[3] Song, S., Miller, K. D. and Abbott, L. F. (2000). Competitive

Hebbian learning through spike-timing-dependent synaptic

plasticity,Nature Neuroscience 3:9,919--926

[4] van Rossum, M. C. W., Bi, G-Q and Turrigiano, G. G. (2000).

Stable Hebbian learning from spike timing-dependent

plasticity, Journal of Neuroscience, 20:23,8812--8821

**Author:**

Moritz Helias, Abigail Morrison

Adapted by: Philipp Weidel

**FirstVersion:**

March 2006

**SeeAlso:**

**Source:**

/home/graber/work-nest/nest-git/nest-simulator/models/stdp_connection.h